IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v615y2023ics0378437123001206.html
   My bibliography  Save this article

New fuzzy multiple regressions for the instantaneous and panel data “The determinants of Poverty in the Countries MENA”

Author

Listed:
  • Belhadj, Besma

Abstract

Habitually, to determine the effect of certain economic and social variables on poverty in MENA, a classical multiple regression with a framework defined a priori verifying several limiting assumptions and easily misused is used. We propose, in this paper, a fuzzy alternative approach to classical multiple regressions with cross-sectional and panel data and explain how the proposed fuzzy methods works in a simple setting. As illustration, we estimates and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and the annual population growth rate on poverty in MENA.

Suggested Citation

  • Belhadj, Besma, 2023. "New fuzzy multiple regressions for the instantaneous and panel data “The determinants of Poverty in the Countries MENA”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    • Handle: RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001206
    DOI: 10.1016/j.physa.2023.128565
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123001206
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.128565?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Besma Belhadj, 2011. "A new fuzzy unidimensional poverty index from an information theory perspective," Empirical Economics, Springer, vol. 40(3), pages 687-704, May.
    3. Besma Belhadj & Firas Kaabi, 2020. "New membership function for poverty measure," Metroeconomica, Wiley Blackwell, vol. 71(4), pages 676-688, November.
    4. D'Urso, Pierpaolo & Gastaldi, Tommaso, 2000. "A least-squares approach to fuzzy linear regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 427-440, October.
    5. World Bank, 2018. "Global Financial Development Report 2017/2018," World Bank Publications - Books, The World Bank Group, number 28482, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoyu Yin & Xiujian Wei & Muhammad Irfan & Sonia Yasin, 2023. "Revitalizing Organizational Efficiency: Unpacking the Relationship between CEO Turnover, Research and Development, and Pay-Performance Sensitivities in the Financial Sector of Pakistan," Sustainability, MDPI, vol. 15(13), pages 1-15, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hillebrand, Eric & Schnabl, Gunther & Ulu, Yasemin, 2009. "Japanese foreign exchange intervention and the yen-to-dollar exchange rate: A simultaneous equations approach using realized volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 490-505, July.
    2. Frederico Belo & Chen Xue & Lu Zhang, 2010. "Cross-sectional Tobin's Q," NBER Working Papers 16336, National Bureau of Economic Research, Inc.
    3. Bansal, Ravi & Kiku, Dana & Yaron, Amir, 2016. "Risks for the long run: Estimation with time aggregation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 52-69.
    4. Alfonso Mendoza-Velázquez & Luis Carlos Ortuño-Barba & Luis David Conde-Cortés, 2022. "Corporate governance and firm performance in hybrid model countries," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 21(1), pages 32-58, February.
    5. Chang, Jinyuan & Chen, Song Xi & Chen, Xiaohong, 2015. "High dimensional generalized empirical likelihood for moment restrictions with dependent data," Journal of Econometrics, Elsevier, vol. 185(1), pages 283-304.
    6. Kutuk, Yasin, 2022. "Inequality convergence: A world-systems theory approach," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 150-165.
    7. Smoluk, H. J. & Neveu, Raymond P., 2002. "Consumption and asset prices: An analysis across income groups," Review of Financial Economics, Elsevier, vol. 11(1), pages 47-62.
    8. Bruce N. Lehmann, 2005. "The Role of Beliefs in Inference for Rational Expectations Models," NBER Working Papers 11758, National Bureau of Economic Research, Inc.
    9. Aslan, Alper & Kaplan, Muhittin & Kula, Ferit, 2008. "International Tourism Demand for Turkey: A Dynamic Panel Data Approach," MPRA Paper 10601, University Library of Munich, Germany.
    10. de Mendonça, Helder Ferreira & Tiberto, Bruno Pires, 2014. "Public debt and social security: Level of formality matters," Economic Modelling, Elsevier, vol. 42(C), pages 490-507.
    11. Alessandra Canepa & Fawaz Khaled, 2018. "Housing, Housing Finance and Credit Risk," IJFS, MDPI, vol. 6(2), pages 1-23, May.
    12. Isaiah Andrews & Anna Mikusheva, 2016. "Conditional Inference With a Functional Nuisance Parameter," Econometrica, Econometric Society, vol. 84, pages 1571-1612, July.
    13. Carrión-Flores, Carmen E. & Innes, Robert, 2010. "Environmental innovation and environmental performance," Journal of Environmental Economics and Management, Elsevier, vol. 59(1), pages 27-42, January.
    14. repec:ebl:ecbull:v:3:y:2007:i:44:p:1-6 is not listed on IDEAS
    15. Coppi, Renato & D'Urso, Pierpaolo & Giordani, Paolo & Santoro, Adriana, 2006. "Least squares estimation of a linear regression model with LR fuzzy response," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 267-286, November.
    16. Jessica M. Mc Lay & Roy Lay-Yee & Barry J. Milne & Peter Davis, 2015. "Regression-Style Models for Parameter Estimation in Dynamic Microsimulation: An Empirical Performance Assessment," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 83-127.
    17. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    18. Jarle Aarstad & Olav Andreas Kvitastein & Stig-Erik Jakobsen, 2019. "What Drives Enterprise Product Innovation? Assessing How Regional, National, And International Inter-Firm Collaboration Complement Or Substitute For R&D Investments," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(05), pages 1-25, June.
    19. Lee, Boram & Rosenthal, Leonard & Veld, Chris & Veld-Merkoulova, Yulia, 2015. "Stock market expectations and risk aversion of individual investors," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 122-131.
    20. Yuichi Kitamura & Jörg Stoye, 2013. "Nonparametric analysis of random utility models: testing," CeMMAP working papers 36/13, Institute for Fiscal Studies.
    21. Alex Cukierman & Anton Muscatelli, 2001. "Do Central Banks have Precautionary Demands for Expansions and for Price Stability?," Working Papers 2002_4, Business School - Economics, University of Glasgow, revised Mar 2002.

    More about this item

    Keywords

    Fuzzy endogenous regressor; Fuzzy parameters; Fuzzy mathematical modeling; Cross-sectional data; Panel data;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001206. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.